Guest Editorial: Computer Vision Research at Microsoft Corporation

2004 ◽  
Vol 58 (2) ◽  
pp. 91-92
Author(s):  
P. Anandan ◽  
Andrew Blake
Keyword(s):  
Computer Vision ◽  
Vision Research ◽  
Guest Editorial ◽  
Microsoft Corporation

scholarly journals Guest Editorial: Generative Adversarial Networks for Computer Vision

2020 ◽  
Vol 128 (10-11) ◽  
pp. 2363-2365
Author(s):  
Jun-Yan Zhu ◽  
Hongsheng Li ◽  
Eli Shechtman ◽  
Ming-Yu Liu ◽  
Jan Kautz ◽  
...  
Keyword(s):  
Computer Vision ◽  
Guest Editorial ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

Multilevel evidence fusion for the recognition of 3-D objects: an overview of computer vision research at IBM/T.J. Watson

1991 ◽  
Author(s):  
Ruud M. Bolle ◽  
Andrea Califano ◽  
John R. Kender ◽  
Rick Kjeldsen ◽  
Rakesh Mohan
Keyword(s):  
Computer Vision ◽  
Vision Research

scholarly journals Subject Bias in Image Aesthetic Appeal Ratings

2017 ◽  
Vol 1 (1) ◽  
pp. 13-20
Author(s):  
Ernestasia Siahaan ◽  
Esther Nababan
Keyword(s):  
Computer Vision ◽  
Confidence Interval ◽  
Vision Research ◽  
Image Content ◽  
Learning Methods ◽  
Content Category ◽  
Content Quality ◽  
Aesthetic Appeal ◽  
The Past ◽  
Image Characteristics

Automatic prediction of image aesthetic appeal is an important part of multimedia and computer vision research, as it contributes to providing better content quality to users. Various features and learning methods have been proposed in the past to predict image aesthetic appeal more accurately. The effectiveness of these proposed methods often depend on the data used to train the predictor. Since aesthetic appeal is a subjective construct, factors that influence the subjectivity in aesthetic appeal data need to be understood and addressed. In this paper, we look into the subjectivity of aesthetic appeal data, and how it relates with image characteristics that are often used in aesthetic appeal prediction. We use subject bias and confidence interval to measure subjectivity, and check how they might be influenced by image content category and features.

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